Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health

U-DESPE：一种基于剂量-暴露的贝叶斯方法，用于早期肿瘤试验中的剂量方案优化

U-DESPE: a Bayesian Utility-based methodology for dosing regimen optimization in early-phase oncology trials based on Dose-Exposure, Safety, Pharmacodynamics, Efficacy

U-DESPE是一种基于剂量-暴露的贝叶斯方法，用于早期肿瘤试验中的剂量方案优化，通过整合药代动力学数据和效用函数，提高剂量选择的科学性和安全性。

Towards personalized causal inference of medication response in mobile health: an instrumental variable approach for randomized trials with imperfect compliance

Mobile health studies can leverage longitudinal sensor data from smartphones to guide the application of personalized medical interventions. In this paper, we propose that adoption of an instrumental variable approach for randomized trials with imperfect compliance provides a natural framework for personalized causal inference of medication response in mobile health studies. Randomized treatment suggestions can be easily delivered to the study participants via electronic messages popping up on the smart-phone screen. Under quite general assumptions we can identify the causal effect of the actu…

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-…

Efficient, Doubly Robust Estimation of the Effect of Dose Switching for Switchers in a Randomised Clinical Trial

Motivated by a clinical trial conducted by Janssen Pharmaceuticals in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose is unsatisfactory because the latter patients are usually in a better health condition. Because the available information in the considered…

利用HeartSteps微随机试验的纵向函数型数据建模移动健康干预的时变效应

Modeling Time-Varying Effects of Mobile Health Interventions Using Longitudinal Functional Data from HeartSteps Micro-Randomized Trial

本文针对HeartSteps微随机试验提出首个半参数变系数因果偏移效应模型，结合双重时间指标和两阶段稳健估计量，揭示移动健康干预效应在决策点内外的时变规律及其情境调节机制。

使用德国医疗数据进行新冠疫苗安全性分析时记录链接错误的影响：一项模拟研究

Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study

本研究通过蒙特卡洛模拟量化德国医疗数据中记录链接错误对新冠疫苗安全性分析方法的影响，发现随机链接错误对检验效能影响小，SCCS方法无偏，Cox模型低估效应。

连续动作下的拟最优强化学习

Quasi-optimal Reinforcement Learning with Continuous Actions

针对连续动作医疗决策中无限支撑策略可能给出危险高剂量的问题，提出一种拟最优RL算法，缩小动作搜索范围，并证明其一致性、样本复杂度、适应性与收敛性。